https://github.com/opendilab/DI-engine/blob/main/ding/policy/qmix.py
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
Papers with Code 아카이브
SMAC
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The StarCraft Multi-Agent Challenge (SMAC) is a benchmark that provides elements of partial observability, challenging dynamics, and high-dimensional observation spaces. SMAC is built using the StarCraft II game engine, creating a testbed for research in cooperative MARL where each game unit is an independent RL agent.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/opendilab/DI-engine/blob/main/ding/policy/qtran.py
QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning
https://github.com/osilab-kaist/smac_plus
https://github.com/oxwhirl/comix
https://github.com/oxwhirl/facmac
https://github.com/petsol/MultiAgentCooperation_UnityAgent_MADDPG_Udacity
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/philtabor/Multi-Agent-Deep-Deterministic-Policy-Gradients
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/pr-shukla/maddpg-keras
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/qi-pang/mdpfuzz
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/quantumiracle/mars
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/rainandwind1/MADDPG-reconstruct
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/rainandwind1/MERL
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/rainandwind1/Maddpg_multiagent
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/rallen10/multiagent-particle-envs
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/raoshashank/Tennis-with-MADDPG
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/ray-project/ray/tree/master/rllib
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/schroederdewitt/multiagent-particle-envs
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/semitable/multiagent-particle-envs
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/shariqiqbal2810/maddpg-pytorch
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/shariqiqbal2810/multiagent-particle-envs
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/simsimiSION/pymarl-algorithm-extension-via-starcraft
https://github.com/sliao-mi-luku/DeepRL-multiple-agents-tennis-udacity-drlnd-p3
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/starry-sky6688/MADDPG
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/starry-sky6688/marl-algorithms
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
https://github.com/sunjing1102628/DOMAC
https://github.com/thechrisyoon08/Multi-agent-reinforcement-learning
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/thechrisyoon08/marl
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/tjuhaoxiaotian/pymarl3
Value-Decomposition Networks For Cooperative Multi-Agent Learning
https://github.com/tkarr21/multagent-particle-envs
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/tkarr21/multiagent-particle-envs
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/uoe-agents/epymarl
https://github.com/wendelinboehmer/dcg
https://github.com/wsjeon/multiagent-particle-envs-maac
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/wsjeon/multiagent-particle-envs-v2
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/xiuyu0000/new_papers_codes/tree/main/qmix
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
https://github.com/xuehy/pytorch-maddpg
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.